SIFC_BSetPTY
MICK
2025-06-24
Sessile Invertebrate Fouling Community Analysis Panamá
In this study, we assess the structure and composition of sessile fouling communities across different coastal regions of Panama. Experimental settlement plates were deployed under varying predation treatments (open, partial, and caged) during distinct seasonal periods. Species richness and biomass were measured on each plate, and community composition was analyzed using point count data. To evaluate spatial and seasonal differences, we applied univariate statistical tests (Kruskal-Wallis and Dunn’s post hoc comparisons) and multivariate analyses including PERMANOVA and a non-metric multidimensional scaling (NMDS). This approach allows us to investigate how regional variability, predation pressure, and seasonal conditions influence the diversity and structure of fouling communities in the Gulf of Panama.
Excel file: ALL_PTY_CR_inland
Notes:
Used RawPlateWeight = 166.5 for Inland Plates
Plates removed because of missing data: 4A, 4B, 195B, 268A, 276A, 285A, 304A, 409B, 530B,1308A, 2041A
Duplicated plates: 228A (deleted 23/11/2022), 527B (first row deleted)
Corrected sites: 1705A_B (changed to 1708B), 1259A (actually 1295A), 217A (originally 2147A)
Richness_Datasheets
V_1: Species Richness
Richness_cleaning_Calculation
A cleaning function was applied to correct for duplicate live and dead records in species richness counts.
cat("Riqueza igual en:", igual, "filas\n")## Riqueza igual en: 824 filascat("Riqueza AUMENTÓ en:", aumentaron, "filas\n")## Riqueza AUMENTÓ en: 0 filascat("Riqueza DISMINUYÓ en:", disminuyeron, "filas\n")## Riqueza DISMINUYÓ en: 12 filasknitr::kable(head(changed), caption = "Preview of Cleaned Dataset")| Plate | SpeciesRichness | Richness_Cleaned | Species_List_Cleaned |
|---|---|---|---|
| 826B | 40 | 39 | Bryo 67 , RAM , GAM , Spyridia , Heterosiphonia, Polysiphonia , RFA , Hydro A , Hydro B , Serp 5 , Spiro CW , Bryopsis 1 , Chlorodesmis , Anthozoa , Pori A , Bryo 80 , Tuni 58 , CCA , Bryo 9 , Bryopsis 2 , Ecto 2 , Gelidiopsis , Cladophora , Biv 7 , Pori C , Tuni 11 , Bryo 5 , Anemone , Amphiroa , Biv , Cirri 3 , Serp 4 , Cirri , Cirri 6 , Tuni , Gastro 3 , Bryo 37 , Gayliella , B |
| 743B | 33 | 32 | Cirri 6 , Cirri 8 , Cirri 11 , Hydro A , Hydro B , Bryo 37 , Heterosiphonia, Bryopsis , Bryo 5 , Tuni 11 , Tuni 30 , Bryo 67 , Pori C , Bryopsis 2 , Biv 7 , Tuni 6 , Bryo 62 , Anemone , Serp , Tuni 8 , Serp 5 , Spiro CCW , Biv 6 , Valonia , Bryo 9 , CCA , Polysiphonia , Pori B , Pori A , RAM , Cirri 15 , B |
| 2079B | 37 | 36 | Bryo 85 , Serp , RAM , BAM , GAM , Cirri 6 , Serp 4 , Bryo 37 , RFA UID 8 , CCA , Pori A , RFA , Hydro C , Hydro B , Bryopsis 1 , Ecto 2 , Spyridia 2 , Biv 2 , Gastro 3 , Spiro CW , Spiro CCW , Serp 5 , Biv 15 , Biv 14 , Dictyota , Polysiphonia, Cirri 15 , Bryo 67 , Cirri 11 , Bryo 72 , Bryo 41 , Spyridia , Pori C , Tuni 58 , Biv 7 , B |
| 994B | 17 | 16 | Tuni 58 , Hydro A , Cirri 6 , Tuni 11 , Serp , Hypnea , Sabellidae, Cirri 11 , Bryo 41 , Tuni 14 , Cladophora, Spiro CW , RAM , Hydro B , Bryo , B |
| 1168B | 19 | 18 | Tuni 8 , Calyptraea , RAM , Serp , Gastro 3 , Cirri 6 dead , Gastro 1 dead , Bryo 41 dead , GAM , BAM , Polysiphonia , Bryo 78 dead , Cirri 3 dead , Bryo 62 , Tuni 30 , Heterosiphonia, Tuni , B |
| 1911B | 35 | 34 | Cirri 6 , Serp , Pori A , Tuni 30 , Bryo 62 , Serp 5 , Hydro B , Spiro CCW , RAM , Tuni 6 , RFA , Tuni 58 , Bryo 72 , Spiro CW , Tuni 47 , CCA , Bryo 5 , Cirri , Cirri 15 , GAM , Spyridia , Asparagopsis , Polysiphonia , Bryo 7 , Biv 14 , Tuni 11 , Hydro A , Cirri 11 , Heterosiphonia, Bryo 33 dead , Serp 4 , Cyanobacteria , Ecto 1 , B |
Filter just for Panama Regions (PP_in_PPLP_PPCOI)
#Filter just for PP regions
B_set_filtered <- B_set %>%
filter(Region %in% c("PP_in", "PPLP", "PPCOI")) Assess_NormalitySpecies Richness: P-value (< 0.001) indicates that the data does not follow a normal distribution
##
## Shapiro-Wilk normality test
##
## data: B_set_filtered$Richness_Cleaned
## W = 0.98259, p-value = 1.56e-06TREATMENT
The Kruskal-Wallis test revealed no significant differences in species richness among predation treatments (χ² = 2.70, df = 2, p = 0.260). Post-hoc comparisons using Dunn’s test and pairwise Wilcoxon tests confirmed the absence of significant differences between treatments after adjusting for multiple comparisons.
##
## Kruskal-Wallis rank sum test
##
## data: Richness_Cleaned by Treatment
## Kruskal-Wallis chi-squared = 2.6958, df = 2, p-value = 0.2598## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Holm method.## Comparison Z P.unadj P.adj
## 1 Caged - Open 0.8954863 0.3705272 0.7410544
## 2 Caged - Partial -0.7854525 0.4321883 0.4321883
## 3 Open - Partial -1.6371990 0.1015889 0.3047667##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: B_set_filtered$Richness_Cleaned and B_set_filtered$Treatment
##
## Caged Open
## Open 1.00 -
## Partial 1.00 0.31
##
## P value adjustment method: bonferroniREGION
The Kruskal-Wallis test detected highly significant differences in species richness among regions (χ² = 156.35, df = 2, p < 0.001), indicating strong spatial heterogeneity. Post-hoc pairwise comparisons using Dunn’s test and Wilcoxon tests confirmed that richness was significantly higher in PP_in compared to both PPCOI and PPLP (adjusted p < 0.001), while no significant difference was observed between PPCOI and PPLP.
##
## Kruskal-Wallis rank sum test
##
## data: Richness_Cleaned by Region
## Kruskal-Wallis chi-squared = 156.35, df = 2, p-value < 2.2e-16## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Holm method.## Comparison Z P.unadj P.adj
## 1 PP_in - PPCOI -11.5087545 1.191873e-30 2.383747e-30
## 2 PP_in - PPLP -11.7877459 4.514628e-32 1.354388e-31
## 3 PPCOI - PPLP -0.4099216 6.818634e-01 6.818634e-01##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: B_set_filtered$Richness_Cleaned and B_set_filtered$Region
##
## PP_in PPCOI
## PPCOI <2e-16 -
## PPLP <2e-16 1
##
## P value adjustment method: bonferroni
SITE
The Kruskal-Wallis test revealed highly significant differences in species richness across sites (χ² = 160.94, df = 9, p < 0.001), indicating pronounced spatial variability at the site level. Post-hoc pairwise comparisons using Dunn’s test and Wilcoxon tests showed multiple significant differences between sites, with several pairwise comparisons remaining highly significant after correction for multiple testing. These results suggest strong site-specific heterogeneity in community richness across the study area.
##
## Kruskal-Wallis rank sum test
##
## data: Richness_Cleaned by Site
## Kruskal-Wallis chi-squared = 160.94, df = 9, p-value < 2.2e-16## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Holm method.## Comparison Z P.unadj P.adj
## 1 ACP_Naos - Bahia Damas -6.02137564 1.729409e-09 5.879990e-08
## 2 ACP_Naos - Balboa Port -0.57549479 5.649567e-01 1.000000e+00
## 3 Bahia Damas - Balboa Port 5.27307270 1.341584e-07 3.353960e-06
## 4 ACP_Naos - Canales -5.75487548 8.670568e-09 2.687876e-07
## 5 Bahia Damas - Canales 0.53300654 5.940291e-01 1.000000e+00
## 6 Balboa Port - Canales -5.00322502 5.637907e-07 1.353098e-05
## 7 ACP_Naos - Contadora -6.13263245 8.643669e-10 3.025284e-08
## 8 Bahia Damas - Contadora -0.18081614 8.565119e-01 1.000000e+00
## 9 Balboa Port - Contadora -5.38294812 7.327567e-08 1.905167e-06
## 10 Canales - Contadora -0.71980755 4.716435e-01 1.000000e+00
## 11 ACP_Naos - Flamenco -1.13545883 2.561831e-01 1.000000e+00
## 12 Bahia Damas - Flamenco 6.24199652 4.320206e-10 1.598476e-08
## 13 Balboa Port - Flamenco -0.47093470 6.376874e-01 1.000000e+00
## 14 Canales - Flamenco 5.90313464 3.566590e-09 1.176975e-07
## 15 Contadora - Flamenco 6.39397182 1.616312e-10 6.303618e-09
## 16 ACP_Naos - Mogo Mogo -6.29366155 3.100635e-10 1.178241e-08
## 17 Bahia Damas - Mogo Mogo -0.48051232 6.308631e-01 1.000000e+00
## 18 Balboa Port - Mogo Mogo -5.54466055 2.945250e-08 8.541225e-07
## 19 Canales - Mogo Mogo -1.02227961 3.066486e-01 1.000000e+00
## 20 Contadora - Mogo Mogo -0.30204609 7.626169e-01 1.000000e+00
## 21 Flamenco - Mogo Mogo -6.60329827 4.021093e-11 1.648648e-09
## 22 ACP_Naos - Naos Dock -0.61850446 5.362429e-01 1.000000e+00
## 23 Bahia Damas - Naos Dock 7.00814114 2.415051e-12 1.038472e-10
## 24 Balboa Port - Naos Dock 0.04601968 9.632946e-01 9.632946e-01
## 25 Canales - Naos Dock 6.67524528 2.468190e-11 1.036640e-09
## 26 Contadora - Naos Dock 7.16257125 7.917769e-13 3.483818e-11
## 27 Flamenco - Naos Dock 0.63313722 5.266440e-01 1.000000e+00
## 28 Mogo Mogo - Naos Dock 7.37068214 1.697572e-13 7.639072e-12
## 29 ACP_Naos - Saboga -5.42594050 5.765016e-08 1.556554e-06
## 30 Bahia Damas - Saboga 1.09519655 2.734305e-01 1.000000e+00
## 31 Balboa Port - Saboga -4.67625616 2.921596e-06 6.427512e-05
## 32 Canales - Saboga 0.57517431 5.651734e-01 1.000000e+00
## 33 Contadora - Saboga 1.28376968 1.992226e-01 1.000000e+00
## 34 Flamenco - Saboga -5.46262741 4.691386e-08 1.313588e-06
## 35 Mogo Mogo - Saboga 1.58196638 1.136572e-01 1.000000e+00
## 36 Naos Dock - Saboga -6.23122684 4.627964e-10 1.666067e-08
## 37 ACP_Naos - Uvas -5.70551427 1.159923e-08 3.479770e-07
## 38 Bahia Damas - Uvas 0.60133005 5.476202e-01 1.000000e+00
## 39 Balboa Port - Uvas -4.95516087 7.227047e-07 1.662221e-05
## 40 Canales - Uvas 0.07240715 9.422779e-01 1.000000e+00
## 41 Contadora - Uvas 0.78745990 4.310127e-01 1.000000e+00
## 42 Flamenco - Uvas -5.83340813 5.430651e-09 1.737808e-07
## 43 Mogo Mogo - Uvas 1.08801783 2.765872e-01 1.000000e+00
## 44 Naos Dock - Uvas -6.60320011 4.023758e-11 1.609503e-09
## 45 Saboga - Uvas -0.50010231 6.170030e-01 1.000000e+00##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: B_set_filtered$Richness_Cleaned and B_set_filtered$Site
##
## ACP_Naos Bahia Damas Balboa Port Canales Contadora Flamenco
## Bahia Damas 8.2e-07 - - - - -
## Balboa Port 1.00000 1.9e-05 - - - -
## Canales 1.2e-06 1.00000 7.4e-06 - - -
## Contadora 4.3e-07 1.00000 8.6e-06 1.00000 - -
## Flamenco 0.01447 1.1e-08 1.00000 8.8e-10 7.3e-09 -
## Mogo Mogo 4.3e-07 1.00000 3.9e-06 1.00000 1.00000 1.2e-09
## Naos Dock 1.00000 6.6e-10 1.00000 1.2e-10 1.3e-10 1.00000
## Saboga 2.5e-05 1.00000 0.00026 1.00000 1.00000 2.7e-06
## Uvas 1.1e-06 1.00000 1.1e-05 1.00000 1.00000 8.5e-09
## Mogo Mogo Naos Dock Saboga
## Bahia Damas - - -
## Balboa Port - - -
## Canales - - -
## Contadora - - -
## Flamenco - - -
## Mogo Mogo - - -
## Naos Dock 4.6e-11 - -
## Saboga 1.00000 1.2e-07 -
## Uvas 1.00000 3.7e-10 1.00000
##
## P value adjustment method: bonferroniSEASON
The Kruskal-Wallis test detected a significant difference in species richness between seasons (χ² = 4.44, df = 1, p = 0.035), indicating seasonal variation in community richness. Pairwise Wilcoxon test with Bonferroni correction confirmed a significant difference between wet and dry seasons (adjusted p = 0.035), with richness varying across seasonal periods.
##
## Kruskal-Wallis rank sum test
##
## data: Richness_Cleaned by Season
## Kruskal-Wallis chi-squared = 4.4428, df = 1, p-value = 0.03505##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: B_set_filtered$Richness_Cleaned and B_set_filtered$Season
##
## Dry
## Wet 0.035
##
## P value adjustment method: bonferroni## # A tibble: 2 × 5
## Season Mean_Richness Median_Richness SD N
## <chr> <dbl> <dbl> <dbl> <int>
## 1 Dry 25.8 26 7.77 283
## 2 Wet 27.3 27 8.21 312Conclusion
Species richness varied significantly across spatial and seasonal scales, but no effects of predation exclusion treatments were detected. Kruskal-Wallis tests indicated no significant differences in richness among treatments (p = 0.26). In contrast, richness differed significantly between region (p < 0.001) and site (p < 0.001), with pairwise comparisons showing that PP_in had significantly lower richness compared to both PPCOI and PPLP, which did not differ from each other. Seasonal analysis revealed significantly higher richness during the wet season (mean = 27.3 species) compared to the dry season (mean = 25.8 species; p = 0.035). While spatial variability was the dominant factor structuring richness patterns, seasonal fluctuations also contributed modestly to community richness, likely reflecting recruitment dynamics and environmental stability.
Variable_2: Biomass
Asses Normality
Visual inspection of the histogram and Q-Q plot indicated a clear deviation from normality in biomass data. The Shapiro-Wilk test confirmed this, showing a significant departure from normal distribution (W = 0.512, p < 0.001), indicating that biomass data are not normally distributed.
##
## Shapiro-Wilk normality test
##
## data: B_set_filtered$Biomass
## W = 0.51185, p-value < 2.2e-16TREATMENT
The Kruskal-Wallis test revealed highly significant differences in biomass among treatments (χ² = 71.83, df = 2, p < 0.001). Post-hoc Dunn’s test showed that biomass was significantly higher in caged plates compared to both open and partial treatments (adjusted p < 0.001). No significant difference was detected between open and partial treatments (adjusted p = 1.0). Pairwise Wilcoxon tests confirmed these results, indicating strong treatment effects on biomass accumulation.
##
## Kruskal-Wallis rank sum test
##
## data: Biomass by Treatment
## Kruskal-Wallis chi-squared = 71.832, df = 2, p-value = 2.523e-16## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 Caged - Open 7.7262313 1.107773e-14 3.323319e-14
## 2 Caged - Partial 6.7992803 1.051431e-11 3.154293e-11
## 3 Open - Partial -0.4548938 6.491856e-01 1.000000e+00##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: B_set$Biomass and B_set$Treatment
##
## Caged Open
## Open 9.2e-12 -
## Partial 5.9e-07 0.54
##
## P value adjustment method: bonferroni
REGION
The Kruskal-Wallis test indicated highly significant differences in biomass among regions (χ² = 153.77, df = 2, p < 0.001). Post-hoc Dunn’s test revealed that biomass differed significantly between all regional comparisons, with especially strong differences between PPCOI and both PP_in and PPLP (adjusted p < 0.001). Pairwise Wilcoxon tests confirmed these strong regional differences in biomass, highlighting substantial spatial variability across regions.
##
## Kruskal-Wallis rank sum test
##
## data: Biomass by Region
## Kruskal-Wallis chi-squared = 153.77, df = 2, p-value < 2.2e-16## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 PP_in - PPCOI 10.650968 1.725610e-26 5.176829e-26
## 2 PP_in - PPLP 2.542342 1.101125e-02 3.303375e-02
## 3 PPCOI - PPLP -10.360423 3.752997e-25 1.125899e-24##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: B_set$Biomass and B_set$Region
##
## CR_GP CR_GSE PP_in PPCOI
## CR_GSE 0.00016 - - -
## PP_in 1.00000 0.33175 - -
## PPCOI < 2e-16 < 2e-16 < 2e-16 -
## PPLP 4.6e-07 1.1e-14 0.00348 < 2e-16
##
## P value adjustment method: bonferroni
SITE
The Kruskal-Wallis test indicated highly significant differences in biomass across sites (χ² = 189.34, df = 9, p < 0.001), demonstrating pronounced site-level variation in biomass accumulation. Post-hoc Dunn’s test and pairwise Wilcoxon tests revealed numerous significant pairwise differences between sites after correction for multiple comparisons. Many sites showed extremely strong differences, especially involving Bahia Damas, Canales, Contadora, Flamenco, and Uvas, highlighting marked spatial heterogeneity in biomass among sites.
##
## Kruskal-Wallis rank sum test
##
## data: Biomass by Site
## Kruskal-Wallis chi-squared = 189.34, df = 9, p-value < 2.2e-16## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 ACP_Naos - Bahia Damas 6.27365430 3.526711e-10 1.587020e-08
## 2 ACP_Naos - Balboa Port 5.06307496 4.125476e-07 1.856464e-05
## 3 Bahia Damas - Balboa Port 0.10871488 9.134286e-01 1.000000e+00
## 4 ACP_Naos - Canales 7.32468359 2.394630e-13 1.077584e-11
## 5 Bahia Damas - Canales 1.66654987 9.560394e-02 1.000000e+00
## 6 Balboa Port - Canales 0.96560498 3.342419e-01 1.000000e+00
## 7 ACP_Naos - Contadora 2.91766003 3.526686e-03 1.587009e-01
## 8 Bahia Damas - Contadora -5.62918836 1.810596e-08 8.147681e-07
## 9 Balboa Port - Contadora -3.60386925 3.135148e-04 1.410817e-02
## 10 Canales - Contadora -7.33160845 2.274067e-13 1.023330e-11
## 11 ACP_Naos - Flamenco 1.83358423 6.671573e-02 1.000000e+00
## 12 Bahia Damas - Flamenco -5.50252033 3.744000e-08 1.684800e-06
## 13 Balboa Port - Flamenco -4.01275115 6.001517e-05 2.700683e-03
## 14 Canales - Flamenco -6.84929089 7.421695e-12 3.339763e-10
## 15 Contadora - Flamenco -1.14027878 2.541702e-01 1.000000e+00
## 16 ACP_Naos - Mogo Mogo 3.48294021 4.959391e-04 2.231726e-02
## 17 Bahia Damas - Mogo Mogo -4.58544658 4.530171e-06 2.038577e-04
## 18 Balboa Port - Mogo Mogo -2.98937753 2.795465e-03 1.257959e-01
## 19 Canales - Mogo Mogo -6.26659173 3.690360e-10 1.660662e-08
## 20 Contadora - Mogo Mogo 0.99155407 3.214151e-01 1.000000e+00
## 21 Flamenco - Mogo Mogo 1.88934140 5.884610e-02 1.000000e+00
## 22 ACP_Naos - Naos Dock 1.07025143 2.845061e-01 1.000000e+00
## 23 Bahia Damas - Naos Dock -6.49388091 8.365288e-11 3.764380e-09
## 24 Balboa Port - Naos Dock -4.74277122 2.108143e-06 9.486644e-05
## 25 Canales - Naos Dock -7.82029715 5.269879e-15 2.371446e-13
## 26 Contadora - Naos Dock -2.21745945 2.659171e-02 1.000000e+00
## 27 Flamenco - Naos Dock -0.91948350 3.578427e-01 1.000000e+00
## 28 Mogo Mogo - Naos Dock -2.94447197 3.235063e-03 1.455778e-01
## 29 ACP_Naos - Saboga 3.53021314 4.152250e-04 1.868513e-02
## 30 Bahia Damas - Saboga -4.53321759 5.809192e-06 2.614136e-04
## 31 Balboa Port - Saboga -2.95082780 3.169236e-03 1.426156e-01
## 32 Canales - Saboga -6.22036781 4.959908e-10 2.231959e-08
## 33 Contadora - Saboga 1.06744498 2.857709e-01 1.000000e+00
## 34 Flamenco - Saboga 1.94906848 5.128725e-02 1.000000e+00
## 35 Mogo Mogo - Saboga 0.07055968 9.437482e-01 1.000000e+00
## 36 Naos Dock - Saboga 3.00579862 2.648844e-03 1.191980e-01
## 37 ACP_Naos - Uvas 7.21103486 5.552824e-13 2.498771e-11
## 38 Bahia Damas - Uvas 1.50471408 1.323976e-01 1.000000e+00
## 39 Balboa Port - Uvas 0.86413451 3.875140e-01 1.000000e+00
## 40 Canales - Uvas -0.15285789 8.785103e-01 1.000000e+00
## 41 Contadora - Uvas 7.13165795 9.916707e-13 4.462518e-11
## 42 Flamenco - Uvas 6.69982117 2.086747e-11 9.390364e-10
## 43 Mogo Mogo - Uvas 6.07630602 1.229826e-09 5.534217e-08
## 44 Naos Dock - Uvas 7.66970515 1.723922e-14 7.757647e-13
## 45 Saboga - Uvas 6.02957344 1.643930e-09 7.397685e-08##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: B_set$Biomass and B_set$Site
##
## ACP_Naos Bahia Damas Balboa Port Canales Contadora Flamenco Hachal
## Bahia Damas 2.7e-07 - - - - - -
## Balboa Port 0.00031 1.00000 - - - - -
## Canales 3.1e-07 0.86429 1.00000 - - - -
## Contadora 0.03209 1.0e-07 0.01605 3.8e-11 - - -
## Flamenco 1.00000 2.7e-06 0.00527 3.6e-08 1.00000 - -
## Hachal 1.00000 1.2e-12 4.9e-06 1.3e-12 0.00088 1.00000 -
## Jicaro 0.40838 1.5e-15 2.4e-06 3.8e-16 0.13781 1.00000 1.00000
## Matapalito 1.00000 8.2e-14 1.0e-05 3.5e-14 0.00062 1.00000 1.00000
## Matapalo 0.00404 1.1e-12 1.0e-05 3.2e-13 1.00000 1.00000 0.00903
## Mogo Mogo 1.2e-05 7.6e-07 0.02537 8.4e-11 1.00000 1.00000 8.6e-08
## Naos Dock 1.00000 4.8e-06 0.00833 3.9e-08 0.07078 1.00000 1.00000
## Saboga 0.00201 4.0e-05 0.12970 2.8e-07 1.00000 1.00000 4.7e-05
## Uvas 3.4e-07 1.00000 1.00000 1.00000 9.5e-11 1.0e-07 1.6e-12
## Jicaro Matapalito Matapalo Mogo Mogo Naos Dock Saboga
## Bahia Damas - - - - - -
## Balboa Port - - - - - -
## Canales - - - - - -
## Contadora - - - - - -
## Flamenco - - - - - -
## Hachal - - - - - -
## Jicaro - - - - - -
## Matapalito 1.00000 - - - - -
## Matapalo 1.00000 0.00582 - - - -
## Mogo Mogo 6.6e-07 8.9e-09 0.01110 - - -
## Naos Dock 1.00000 1.00000 1.00000 0.01243 - -
## Saboga 0.00194 8.7e-06 1.00000 1.00000 0.01904 -
## Uvas 9.0e-16 6.4e-14 7.0e-13 5.2e-10 7.3e-08 3.5e-07
##
## P value adjustment method: bonferroniSEASON
The Kruskal-Wallis test showed highly significant differences in biomass between seasons (χ² = 41.96, df = 1, p < 0.001). Post-hoc Dunn’s test and pairwise Wilcoxon tests confirmed significantly higher biomass in one season compared to the other (adjusted p < 0.001), indicating strong seasonal variability in biomass accumulation.
##
## Kruskal-Wallis rank sum test
##
## data: Biomass by Season
## Kruskal-Wallis chi-squared = 41.961, df = 1, p-value = 9.309e-11## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Bonferroni method.## Comparison Z P.unadj P.adj
## 1 Dry - Wet 6.477753 9.309844e-11 9.309844e-11##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: B_set$Biomass and B_set$Season
##
## Dry
## Wet 4.7e-13
##
## P value adjustment method: bonferroni
Conclusion
Biomass exhibited significant variability across treatments, regions, sites, and seasons. The data were not normally distributed, as confirmed by the Shapiro-Wilk test. Predation exclusion had a strong effect on biomass accumulation, with significantly higher biomass observed in caged treatments compared to open and partial treatments. Biomass also varied significantly across regions and sites, with multiple pairwise comparisons showing highly significant differences, reflecting substantial spatial heterogeneity. Seasonal variation was evident, with significantly higher biomass detected between seasons. Overall, biomass patterns reflect strong effects of both biotic (predation) and abiotic (spatial and seasonal) factors on community accumulation processes.
nMDS_SpRichness
Region is the strongest and most consistent driver — both for richness, biomass, and likely for composition. Season has a weaker but still significant effect.Treatment affects biomass strongly but not richness — so composition may show treatment effects driven by dominant species biomass (likely shifts in structure).
## [1] "Region" "Site" "Plate"
## [4] "Treatment" "Season" "Biomass"
## [7] "Date" "Observer" "...9"
## [10] "...10" "...11" "...12"
## [13] "...13" "...14" "...15"
## [16] "...16" "...17" "...18"
## [19] "...19" "...20" "...21"
## [22] "...22" "...23" "...24"
## [25] "...25" "...26" "...27"
## [28] "...28" "...29" "...30"
## [31] "...31" "...32" "...33"
## [34] "...34" "...35" "...36"
## [37] "...37" "...38" "...39"
## [40] "...40" "...41" "...42"
## [43] "...43" "...44" "...45"
## [46] "...46" "...47" "...48"
## [49] "...49" "...50" "...51"
## [52] "...52" "...53" "...54"
## [55] "...55" "...56" "...57"
## [58] "...58" "...59" "...60"
## [61] "...61" "PlateType" "SpeciesRichness"
## [64] "Species_List_Cleaned" "Richness_Cleaned"## Run 0 stress 0.2023652
## Run 1 stress 0.2210141
## Run 2 stress 0.2038708
## Run 3 stress 0.212208
## Run 4 stress 0.2131847
## Run 5 stress 0.2115024
## Run 6 stress 0.2139061
## Run 7 stress 0.2072838
## Run 8 stress 0.2224218
## Run 9 stress 0.213604
## Run 10 stress 0.2091674
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## Run 15 stress 0.2211322
## Run 16 stress 0.2067055
## Run 17 stress 0.2072443
## Run 18 stress 0.2233959
## Run 19 stress 0.2139048
## Run 20 stress 0.2126591
## Run 21 stress 0.2213392
## Run 22 stress 0.2141766
## Run 23 stress 0.2128758
## Run 24 stress 0.2150407
## Run 25 stress 0.2052189
## Run 26 stress 0.2114395
## Run 27 stress 0.2068184
## Run 28 stress 0.2138383
## Run 29 stress 0.2089719
## Run 30 stress 0.2061097
## Run 31 stress 0.2098484
## Run 32 stress 0.208575
## Run 33 stress 0.2131315
## Run 34 stress 0.2091747
## Run 35 stress 0.2064516
## Run 36 stress 0.2047453
## Run 37 stress 0.2087709
## Run 38 stress 0.2036788
## Run 39 stress 0.2171607
## Run 40 stress 0.2098238
## Run 41 stress 0.2142038
## Run 42 stress 0.2152072
## Run 43 stress 0.2103084
## Run 44 stress 0.2156385
## Run 45 stress 0.2100868
## Run 46 stress 0.2173505
## Run 47 stress 0.2071218
## Run 48 stress 0.2137769
## Run 49 stress 0.2146308
## Run 50 stress 0.203872
## Run 51 stress 0.2082651
## Run 52 stress 0.220985
## Run 53 stress 0.2150177
## Run 54 stress 0.2120225
## Run 55 stress 0.2120174
## Run 56 stress 0.2116875
## Run 57 stress 0.2089475
## Run 58 stress 0.2111582
## Run 59 stress 0.2099178
## Run 60 stress 0.2192248
## Run 61 stress 0.2160511
## Run 62 stress 0.2056775
## Run 63 stress 0.2124527
## Run 64 stress 0.2075704
## Run 65 stress 0.2099789
## Run 66 stress 0.2253042
## Run 67 stress 0.2099893
## Run 68 stress 0.2092894
## Run 69 stress 0.2036635
## Run 70 stress 0.2066689
## Run 71 stress 0.2106343
## Run 72 stress 0.2230366
## Run 73 stress 0.2108489
## Run 74 stress 0.210201
## Run 75 stress 0.2131207
## Run 76 stress 0.2124463
## Run 77 stress 0.2132557
## Run 78 stress 0.2187091
## Run 79 stress 0.2188272
## Run 80 stress 0.2057905
## Run 81 stress 0.2034614
## Run 82 stress 0.2071094
## Run 83 stress 0.2118381
## Run 84 stress 0.2160218
## Run 85 stress 0.2092652
## Run 86 stress 0.2142329
## Run 87 stress 0.2109895
## Run 88 stress 0.2087134
## Run 89 stress 0.2169257
## Run 90 stress 0.2186669
## Run 91 stress 0.2034218
## Run 92 stress 0.2115131
## Run 93 stress 0.2126123
## Run 94 stress 0.2124107
## Run 95 stress 0.2185426
## Run 96 stress 0.2107161
## Run 97 stress 0.2254039
## Run 98 stress 0.2087329
## Run 99 stress 0.2136234
## Run 100 stress 0.218969
## *** Best solution was not repeated -- monoMDS stopping criteria:
## 26: no. of iterations >= maxit
## 65: stress ratio > sratmax
## 9: scale factor of the gradient < sfgrmin## [1] 0.2023652REGION
SITE
SEASON
PERMANOVA
Region remains the main driver of community composition (explaining ~28% of variation).
Season and Treatment both explain ~3% of variation each — small but significant effects.
Interactions suggest that predation effects may vary across seasons and regions.
## [1] 595## [1] 595## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 100
##
## adonis2(formula = comm_mat ~ Treatment * Season * Region, data = metadata, permutations = 100, method = "bray")
## Df SumOfSqs R2 F Pr(>F)
## Treatment 2 3.491 0.02955 14.2466 0.009901 **
## Season 1 3.521 0.02980 28.7357 0.009901 **
## Region 2 33.396 0.28261 136.2726 0.009901 **
## Treatment:Season 2 0.577 0.00488 2.3538 0.009901 **
## Treatment:Region 4 2.418 0.02046 4.9336 0.009901 **
## Season:Region 2 3.413 0.02889 13.9287 0.009901 **
## Treatment:Season:Region 4 0.651 0.00551 1.3286 0.108911
## Residual 577 70.701 0.59831
## Total 594 118.169 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
——————————————————————————————————————————————–
PointCount_Datasheets
Load data
## tibble [6,799 × 59] (S3: tbl_df/tbl/data.frame)
## $ Region : chr [1:6799] "PPLP" "PPLP" "PPLP" "PPLP" ...
## $ Site : chr [1:6799] "Mogo Mogo" "Mogo Mogo" "Mogo Mogo" "Mogo Mogo" ...
## $ Plate : chr [1:6799] "65A" "65A" "65A" "65A" ...
## $ Treatment : chr [1:6799] "Open" "Open" "Open" "Open" ...
## $ Wet weight: chr [1:6799] "204.0" "204.0" "204.0" "204.0" ...
## $ Season : chr [1:6799] "Dry" "Dry" "Dry" "Dry" ...
## $ Date : POSIXct[1:6799], format: "2022-05-18" "2022-05-18" ...
## $ Observer : chr [1:6799] "AJS" "AJS" "AJS" "AJS" ...
## $ Layer : chr [1:6799] "Primary" "Secondary" "Tertiary" "Canopy" ...
## $ A1 : chr [1:6799] "Bare" NA NA "GFA" ...
## $ A2 : chr [1:6799] "GFA" NA NA NA ...
## $ A3 : chr [1:6799] "BAM" NA NA NA ...
## $ A4 : chr [1:6799] "GFA" NA NA NA ...
## $ A5 : chr [1:6799] "GFA" NA NA NA ...
## $ A6 : chr [1:6799] "GFA" NA NA NA ...
## $ A7 : chr [1:6799] "GFA" NA NA NA ...
## $ B1 : chr [1:6799] "GFA" NA NA NA ...
## $ B2 : chr [1:6799] "GFA" NA NA NA ...
## $ B3 : chr [1:6799] "GFA" NA NA NA ...
## $ B4 : chr [1:6799] "Bare" NA NA "GFA" ...
## $ B5 : chr [1:6799] "BAM" "GFA" NA NA ...
## $ B6 : chr [1:6799] "BAM" "GFA" NA NA ...
## $ B7 : chr [1:6799] "BAM" NA NA NA ...
## $ C1 : chr [1:6799] "GFA" NA NA NA ...
## $ C2 : chr [1:6799] "GFA" NA NA NA ...
## $ C3 : chr [1:6799] "GFA" NA NA NA ...
## $ C4 : chr [1:6799] "BAM" "GFA" NA NA ...
## $ C5 : chr [1:6799] "Polysiphonia" NA NA NA ...
## $ C6 : chr [1:6799] "BAM" NA NA NA ...
## $ C7 : chr [1:6799] "BAM" "GFA" NA NA ...
## $ D1 : chr [1:6799] "BAM" "GFA" NA NA ...
## $ D2 : chr [1:6799] "GFA" NA NA NA ...
## $ D3 : chr [1:6799] "BAM" NA NA NA ...
## $ D4 : chr [1:6799] "BAM" "GFA" NA NA ...
## $ D5 : chr [1:6799] "BAM" "GFA" NA NA ...
## $ D6 : chr [1:6799] "GFA" NA NA NA ...
## $ D7 : chr [1:6799] "GFA" NA NA NA ...
## $ E1 : chr [1:6799] "GFA" NA NA NA ...
## $ E2 : chr [1:6799] "GFA" NA NA NA ...
## $ E3 : chr [1:6799] "BAM" "GFA" NA NA ...
## $ E4 : chr [1:6799] "GFA" NA NA NA ...
## $ E5 : chr [1:6799] "GFA" NA NA NA ...
## $ E6 : chr [1:6799] "BAM" "GFA" NA NA ...
## $ E7 : chr [1:6799] "GFA" NA NA NA ...
## $ F1 : chr [1:6799] "GFA" NA NA NA ...
## $ F2 : chr [1:6799] "BAM" "GFA" NA NA ...
## $ F3 : chr [1:6799] "BAM" "GFA" NA NA ...
## $ F4 : chr [1:6799] "GFA" NA NA NA ...
## $ F5 : chr [1:6799] "GFA" NA NA NA ...
## $ F6 : chr [1:6799] "Polysiphonia" NA NA NA ...
## $ F7 : chr [1:6799] "GFA" NA NA NA ...
## $ G1 : chr [1:6799] "BAM" "GFA" NA NA ...
## $ G2 : chr [1:6799] "BAM" "GFA" NA NA ...
## $ G3 : chr [1:6799] "BAM" NA NA NA ...
## $ G4 : chr [1:6799] "GFA" NA NA NA ...
## $ G5 : chr [1:6799] "BAM" NA NA NA ...
## $ G6 : chr [1:6799] "GFA" NA NA NA ...
## $ G7 : chr [1:6799] "BAM" "GFA" NA NA ...
## $ H : chr [1:6799] "GFA" NA NA NA ...Filter just por Panama Region
1 )Calculate Species Richness
knitr::kable(head(richness_per_plate), caption = "Richness per plate")| Plate | Richness |
|---|---|
| 1001B | 21 |
| 1002B | 17 |
| 1004B | 15 |
| 1005B | 15 |
| 1008B | 16 |
| 1009B | 13 |
2) Calculate Species Abundance per plate
abundance_per_plate <- inverts_long %>%
group_by(Plate, Species) %>%
summarise(Abundance = n()) %>%
ungroup()knitr::kable(head(abundance_per_plate), caption = "Abundance per plate")| Plate | Species | Abundance |
|---|---|---|
| 1001B | AT | 2 |
| 1001B | Bare | 9 |
| 1001B | Biv 2 | 1 |
| 1001B | Bryo 5 | 4 |
| 1001B | Bryo 62 | 3 |
| 1001B | Bryo 67 | 3 |
3) Calculate abundance per species per plate by layers
## # A tibble: 6 × 4
## Plate Layer Species Abundance
## <chr> <chr> <chr> <int>
## 1 1001B Canopy Polysiphonia 1
## 2 1001B Primary AT 1
## 3 1001B Primary Bare 9
## 4 1001B Primary Biv 2 1
## 5 1001B Primary Bryo 5 1
## 6 1001B Primary Bryo 62 3knitr::kable(head(abundance_per_plate_layer), caption = "Richness per plate by layer")| Plate | Layer | Species | Abundance |
|---|---|---|---|
| 1001B | Canopy | Polysiphonia | 1 |
| 1001B | Primary | AT | 1 |
| 1001B | Primary | Bare | 9 |
| 1001B | Primary | Biv 2 | 1 |
| 1001B | Primary | Bryo 5 | 1 |
| 1001B | Primary | Bryo 62 | 3 |
Species Richness –Abundance Set
The p-value is extremely small → Richness is not normally distributed.
##
## Shapiro-Wilk normality test
##
## data: richness_data$Richness
## W = 0.98158, p-value < 2.2e-16TREATMENT
The Kruskal-Wallis test revealed significant differences in species richness among treatments (χ² = 10.44, df = 2, p = 0.005). Post-hoc Dunn’s test indicated that richness was significantly higher in Caged compared to Partial treatments (adjusted p = 0.0046), while differences between Caged and Open were marginal (adjusted p = 0.08) and no significant difference was found between Open and Partial (adjusted p = 0.24). Pairwise Wilcoxon tests confirmed significant differences between Caged and Partial (adjusted p = 0.0045), but not between Caged and Open or Open and Partial.
##
## Kruskal-Wallis rank sum test
##
## data: Richness by Treatment
## Kruskal-Wallis chi-squared = 10.444, df = 2, p-value = 0.005398## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Holm method.## Comparison Z P.unadj P.adj
## 1 Caged - Open 2.054398 0.039937209 0.079874418
## 2 Caged - Partial 3.170959 0.001519364 0.004558093
## 3 Open - Partial 1.183406 0.236648139 0.236648139##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: richness_data$Richness and richness_data$Treatment
##
## Caged Open
## Open 0.1201 -
## Partial 0.0045 0.7119
##
## P value adjustment method: bonferroni
REGION
The Kruskal-Wallis test detected highly significant differences in species richness among regions (χ² = 192.01, df = 2, p < 0.001). Post-hoc Dunn’s test indicated that all pairwise regional comparisons were significant after correction, with the strongest differences observed between PP_in and PPLP (adjusted p < 0.001), and between PPCOI and PPLP (adjusted p < 0.001). Pairwise Wilcoxon tests confirmed these strong differences, with PPLP consistently showing higher richness compared to both PP_in and PPCOI.
##
## Kruskal-Wallis rank sum test
##
## data: Richness by Region
## Kruskal-Wallis chi-squared = 192.01, df = 2, p-value < 2.2e-16## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Holm method.## Comparison Z P.unadj P.adj
## 1 PP_in - PPCOI -4.147394 3.362814e-05 3.362814e-05
## 2 PP_in - PPLP -11.994584 3.793170e-33 1.137951e-32
## 3 PPCOI - PPLP -10.828159 2.531944e-27 5.063889e-27##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: richness_data$Richness and richness_data$Region
##
## PP_in PPCOI
## PPCOI 0.0028 -
## PPLP <2e-16 <2e-16
##
## P value adjustment method: bonferroni
SITE
The Kruskal-Wallis test revealed highly significant differences in species richness among sites (χ² = 210.94, df = 9, p < 0.001), indicating strong site-level variation. Post-hoc Dunn’s test identified multiple significant pairwise differences, especially involving ACP_Naos, Contadora, Mogo Mogo, Saboga, and Uvas, where richness levels were particularly distinct. Pairwise Wilcoxon tests confirmed these patterns, with several site comparisons remaining significant after Bonferroni correction. These results reflect substantial spatial heterogeneity in species richness across sites.
##
## Kruskal-Wallis rank sum test
##
## data: Richness by Site
## Kruskal-Wallis chi-squared = 210.94, df = 9, p-value < 2.2e-16## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Holm method.## Comparison Z P.unadj P.adj
## 1 ACP_Naos - Bahia Damas -4.61173234 3.993270e-06 9.184520e-05
## 2 ACP_Naos - Balboa Port -1.44253972 1.491502e-01 1.000000e+00
## 3 Bahia Damas - Balboa Port 2.73603036 6.218529e-03 1.181520e-01
## 4 ACP_Naos - Canales -4.74175559 2.118741e-06 5.084979e-05
## 5 Bahia Damas - Canales -0.19870387 8.424944e-01 1.000000e+00
## 6 Balboa Port - Canales -2.85766267 4.267738e-03 8.535476e-02
## 7 ACP_Naos - Contadora -7.51928933 5.507478e-14 2.368216e-12
## 8 Bahia Damas - Contadora -5.23455283 1.653848e-07 4.630773e-06
## 9 Balboa Port - Contadora -5.64012473 1.699270e-08 5.437665e-07
## 10 Canales - Contadora -5.11243658 3.180298e-07 8.268774e-06
## 11 ACP_Naos - Flamenco -2.91374541 3.571210e-03 7.499541e-02
## 12 Bahia Damas - Flamenco 1.75126888 7.989961e-02 9.587953e-01
## 13 Balboa Port - Flamenco -1.24804402 2.120149e-01 1.000000e+00
## 14 Canales - Flamenco 1.90935377 5.621647e-02 7.308141e-01
## 15 Contadora - Flamenco 5.57750948 2.439865e-08 7.319595e-07
## 16 ACP_Naos - Mogo Mogo -8.07543839 6.723465e-16 2.958324e-14
## 17 Bahia Damas - Mogo Mogo -6.23352401 4.560574e-10 1.687412e-08
## 18 Balboa Port - Mogo Mogo -6.19798664 5.719000e-10 2.058840e-08
## 19 Canales - Mogo Mogo -6.12630794 8.994166e-10 3.147958e-08
## 20 Contadora - Mogo Mogo -1.02060904 3.074397e-01 1.000000e+00
## 21 Flamenco - Mogo Mogo -6.31016843 2.787320e-10 1.059181e-08
## 22 ACP_Naos - Naos Dock -2.71846902 6.558480e-03 1.180526e-01
## 23 Bahia Damas - Naos Dock 2.04067537 4.128311e-02 5.779635e-01
## 24 Balboa Port - Naos Dock -1.05276763 2.924475e-01 1.000000e+00
## 25 Canales - Naos Dock 2.20101388 2.773504e-02 4.160256e-01
## 26 Contadora - Naos Dock 5.86784326 4.415003e-09 1.501101e-07
## 27 Flamenco - Naos Dock 0.23916375 8.109786e-01 1.000000e+00
## 28 Mogo Mogo - Naos Dock 6.60004304 4.110384e-11 1.603050e-09
## 29 ACP_Naos - Saboga -7.46840853 8.117051e-14 3.327991e-12
## 30 Bahia Damas - Saboga -5.14268175 2.708442e-07 7.312794e-06
## 31 Balboa Port - Saboga -5.58924392 2.280604e-08 7.069873e-07
## 32 Canales - Saboga -5.01919975 5.188717e-07 1.297179e-05
## 33 Contadora - Saboga 0.09242957 9.263567e-01 9.263567e-01
## 34 Flamenco - Saboga -5.51045402 3.579093e-08 1.037937e-06
## 35 Mogo Mogo - Saboga 1.11276146 2.658109e-01 1.000000e+00
## 36 Naos Dock - Saboga -5.80078780 6.600408e-09 2.178135e-07
## 37 ACP_Naos - Uvas -3.38354865 7.155553e-04 1.574222e-02
## 38 Bahia Damas - Uvas 2.23299735 2.554912e-02 4.087859e-01
## 39 Balboa Port - Uvas -1.50438405 1.324825e-01 1.000000e+00
## 40 Canales - Uvas 2.46612595 1.365833e-02 2.321916e-01
## 41 Contadora - Uvas 7.51294604 5.781136e-14 2.428077e-12
## 42 Flamenco - Uvas -0.12704556 8.989043e-01 1.000000e+00
## 43 Mogo Mogo - Uvas 8.51102745 1.723988e-17 7.757947e-16
## 44 Naos Dock - Uvas -0.41737934 6.764010e-01 1.000000e+00
## 45 Saboga - Uvas 7.42051646 1.166645e-13 4.666580e-12##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: richness_data$Richness and richness_data$Site
##
## ACP_Naos Bahia Damas Balboa Port Canales Contadora Flamenco
## Bahia Damas 5.9e-05 - - - - -
## Balboa Port 1.00000 0.12630 - - - -
## Canales 0.00123 1.00000 0.52907 - - -
## Contadora 2.4e-15 6.1e-07 2.6e-08 3.8e-05 - -
## Flamenco 0.00335 1.00000 1.00000 1.00000 4.6e-09 -
## Mogo Mogo 6.9e-14 2.3e-08 2.3e-08 2.1e-07 1.00000 4.8e-10
## Naos Dock 0.00111 1.00000 1.00000 1.00000 2.9e-10 1.00000
## Saboga 1.8e-11 6.7e-06 1.3e-05 0.00032 1.00000 2.4e-07
## Uvas 1.00000 0.23314 1.00000 1.00000 7.5e-12 1.00000
## Mogo Mogo Naos Dock Saboga
## Bahia Damas - - -
## Balboa Port - - -
## Canales - - -
## Contadora - - -
## Flamenco - - -
## Mogo Mogo - - -
## Naos Dock 2.4e-11 - -
## Saboga 1.00000 6.0e-09 -
## Uvas 1.3e-13 1.00000 4.2e-09
##
## P value adjustment method: bonferroni
SEASON
The Kruskal-Wallis test indicated no significant difference in species richness between seasons (χ² = 2.91, df = 1, p = 0.088). Consistent results were obtained with the Wilcoxon test and pairwise comparisons, which also showed no significant seasonal effect on species richness (adjusted p = 0.088).
##
## Kruskal-Wallis rank sum test
##
## data: Richness by Season
## Kruskal-Wallis chi-squared = 2.9101, df = 1, p-value = 0.08803##
## Wilcoxon rank sum test with continuity correction
##
## data: Richness by Season
## W = 734056, p-value = 0.08803
## alternative hypothesis: true location shift is not equal to 0##
## Pairwise comparisons using Wilcoxon rank sum test with continuity correction
##
## data: richness_data$Richness and richness_data$Season
##
## Dry
## Wet 0.088
##
## P value adjustment method: bonferroni
Abundance
The Shapiro-Wilk test indicated that total abundance data were not normally distributed (W = 0.898, p < 0.001), suggesting the use of non-parametric methods for subsequent analyses.
##
## Shapiro-Wilk normality test
##
## data: total_abundance_per_plate$TotalAbundance
## W = 0.89762, p-value < 2.2e-16TREATMENT
The Kruskal-Wallis test detected highly significant differences in total abundance among treatments (χ² = 36.81, df = 2, p < 0.001). Post-hoc Dunn’s test revealed significantly higher total abundance in caged plates compared to both open (adjusted p < 0.001) and partial treatments (adjusted p < 0.001), while no significant difference was found between open and partial treatments (adjusted p = 0.33). These results indicate a strong effect of predation exclusion on total abundance.
##
## Kruskal-Wallis rank sum test
##
## data: TotalAbundance by Treatment
## Kruskal-Wallis chi-squared = 147.41, df = 2, p-value < 2.2e-16## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Holm method.## Comparison Z P.unadj P.adj
## 1 Caged - Open 11.417198 3.431188e-30 1.029356e-29
## 2 Caged - Partial 9.055795 1.355769e-19 2.711539e-19
## 3 Open - Partial -1.933122 5.322120e-02 5.322120e-02
REGION
The Kruskal-Wallis test revealed significant differences in total abundance among regions (χ² = 29.35, df = 2, p < 0.001). Post-hoc Dunn’s test indicated significant differences between PPCOI and both PP_in (adjusted p = 0.00015) and PPLP (adjusted p < 0.001), while no significant difference was detected between PP_in and PPLP (adjusted p = 0.67). These results highlight regional variability in total abundance, particularly between PPCOI and the other regions.
##
## Kruskal-Wallis rank sum test
##
## data: TotalAbundance by Region
## Kruskal-Wallis chi-squared = 117.56, df = 2, p-value < 2.2e-16## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Holm method.## Comparison Z P.unadj P.adj
## 1 PP_in - PPCOI 7.938665 2.043685e-15 4.087371e-15
## 2 PP_in - PPLP 0.856856 3.915245e-01 3.915245e-01
## 3 PPCOI - PPLP -9.755212 1.752341e-22 5.257024e-22
SITE
The Kruskal-Wallis test indicated highly significant differences in total abundance across sites (χ² = 247.22, df = 9, p < 0.001). Post-hoc Dunn’s test revealed numerous significant pairwise differences, particularly involving Balboa Port, Contadora, Mogo Mogo, Naos Dock, and Uvas, where several site comparisons remained highly significant after adjustment. These results demonstrate strong spatial variability in total abundance among sites.
##
## Kruskal-Wallis rank sum test
##
## data: TotalAbundance by Site
## Kruskal-Wallis chi-squared = 247.22, df = 9, p-value < 2.2e-16## Dunn (1964) Kruskal-Wallis multiple comparison## p-values adjusted with the Holm method.## Comparison Z P.unadj P.adj
## 1 ACP_Naos - Bahia Damas 1.13454952 2.565641e-01 7.696923e-01
## 2 ACP_Naos - Balboa Port 5.05966002 4.200047e-07 1.176013e-05
## 3 Bahia Damas - Balboa Port 5.44441270 5.197659e-08 1.507321e-06
## 4 ACP_Naos - Canales 2.99475184 2.746682e-03 4.394691e-02
## 5 Bahia Damas - Canales 3.36970326 7.524917e-04 1.354485e-02
## 6 Balboa Port - Canales -3.61364133 3.019267e-04 5.736607e-03
## 7 ACP_Naos - Contadora -1.43942697 1.500296e-01 7.501479e-01
## 8 Bahia Damas - Contadora -4.65138902 3.297067e-06 8.242667e-05
## 9 Balboa Port - Contadora -8.03053422 9.704914e-16 3.687867e-14
## 10 Canales - Contadora -8.11108266 5.017071e-16 1.956658e-14
## 11 ACP_Naos - Flamenco -2.28433599 2.235179e-02 2.682215e-01
## 12 Bahia Damas - Flamenco -4.87865733 1.068104e-06 2.883882e-05
## 13 Balboa Port - Flamenco -8.12672813 4.410331e-16 1.808236e-14
## 14 Canales - Flamenco -7.36625119 1.754930e-13 6.317748e-12
## 15 Contadora - Flamenco -1.49930312 1.337950e-01 8.027701e-01
## 16 ACP_Naos - Mogo Mogo -1.53740142 1.241950e-01 8.693653e-01
## 17 Bahia Damas - Mogo Mogo -4.81651409 1.460879e-06 3.798284e-05
## 18 Balboa Port - Mogo Mogo -8.12250092 4.566741e-16 1.826696e-14
## 19 Canales - Mogo Mogo -8.26788529 1.363560e-16 5.726952e-15
## 20 Contadora - Mogo Mogo -0.17998608 8.571635e-01 1.000000e+00
## 21 Flamenco - Mogo Mogo 1.36617072 1.718854e-01 6.875415e-01
## 22 ACP_Naos - Naos Dock -4.31724748 1.579870e-05 3.317727e-04
## 23 Bahia Damas - Naos Dock -7.89150389 2.985665e-15 1.104696e-13
## 24 Balboa Port - Naos Dock -10.15963962 3.001839e-24 1.320809e-22
## 25 Canales - Naos Dock -10.40255898 2.413627e-25 1.086132e-23
## 26 Contadora - Naos Dock -4.52180317 6.131506e-06 1.471562e-04
## 27 Flamenco - Naos Dock -2.48979792 1.278157e-02 1.661605e-01
## 28 Mogo Mogo - Naos Dock -4.38389060 1.165783e-05 2.564723e-04
## 29 ACP_Naos - Saboga -0.05340017 9.574131e-01 9.574131e-01
## 30 Bahia Damas - Saboga -2.14876000 3.165343e-02 3.481877e-01
## 31 Balboa Port - Saboga -6.64450743 3.042328e-11 1.064815e-09
## 32 Canales - Saboga -5.57124965 2.529186e-08 7.587558e-07
## 33 Contadora - Saboga 2.51784271 1.180760e-02 1.653065e-01
## 34 Flamenco - Saboga 3.32593821 8.812146e-04 1.498065e-02
## 35 Mogo Mogo - Saboga 2.69027902 7.139230e-03 1.070885e-01
## 36 Naos Dock - Saboga 6.34843825 2.175117e-10 6.960375e-09
## 37 ACP_Naos - Uvas 2.09771539 3.593029e-02 3.593029e-01
## 38 Bahia Damas - Uvas 1.73532381 8.268347e-02 7.441512e-01
## 39 Balboa Port - Uvas -4.49339187 7.009762e-06 1.612245e-04
## 40 Canales - Uvas -1.62942517 1.032230e-01 8.257843e-01
## 41 Contadora - Uvas 6.42553819 1.314038e-10 4.336324e-09
## 42 Flamenco - Uvas 6.16087847 7.234250e-10 2.242617e-08
## 43 Mogo Mogo - Uvas 6.58625724 4.510521e-11 1.533577e-09
## 44 Naos Dock - Uvas 9.18337851 4.177738e-20 1.796427e-18
## 45 Saboga - Uvas 3.90769548 9.318064e-05 1.863613e-03
SEASON
The Kruskal-Wallis test detected highly significant differences in total abundance between seasons (χ² = 218.93, df = 1, p < 0.001). The Wilcoxon test confirmed this strong seasonal effect (p < 0.001), indicating substantial differences in total abundance across seasonal periods.
##
## Kruskal-Wallis rank sum test
##
## data: TotalAbundance by Season
## Kruskal-Wallis chi-squared = 218.93, df = 1, p-value < 2.2e-16##
## Wilcoxon rank sum test with continuity correction
##
## data: TotalAbundance by Season
## W = 952928, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
nMDS_Abundance
community_matrix <- abundance_per_plate %>%
pivot_wider(names_from = Species, values_from = Abundance, values_fill = 0) %>%
column_to_rownames("Plate")
# Build correct metadata to match community_matrix rows
metadata <- B_Pset_filtered %>%
select(Plate, Treatment, Region, Season, Site, PlateType) %>%
distinct(Plate, .keep_all = TRUE) %>%
filter(Plate %in% rownames(community_matrix)) %>%
arrange(match(Plate, rownames(community_matrix)))
# Confirm same number of rows
nrow(metadata)## [1] 594nrow(community_matrix)## [1] 594## run NMDS
set.seed(123)
nmds <- metaMDS(community_matrix, distance = "bray", k = 2, trymax = 100)## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.2477358
## Run 1 stress 0.2518256
## Run 2 stress 0.2477612
## ... Procrustes: rmse 0.01394292 max resid 0.1154555
## Run 3 stress 0.2481311
## ... Procrustes: rmse 0.01095165 max resid 0.09621675
## Run 4 stress 0.2488304
## Run 5 stress 0.2512296
## Run 6 stress 0.2497195
## Run 7 stress 0.2504445
## Run 8 stress 0.2527675
## Run 9 stress 0.2567526
## Run 10 stress 0.2511867
## Run 11 stress 0.2506455
## Run 12 stress 0.247724
## ... New best solution
## ... Procrustes: rmse 0.01182451 max resid 0.1165313
## Run 13 stress 0.2516001
## Run 14 stress 0.2491978
## Run 15 stress 0.2514479
## Run 16 stress 0.2518684
## Run 17 stress 0.251678
## Run 18 stress 0.2552202
## Run 19 stress 0.2566256
## Run 20 stress 0.2498052
## Run 21 stress 0.255156
## Run 22 stress 0.2500519
## Run 23 stress 0.2513924
## Run 24 stress 0.2512628
## Run 25 stress 0.2560964
## Run 26 stress 0.2522982
## Run 27 stress 0.250237
## Run 28 stress 0.2495072
## Run 29 stress 0.2520912
## Run 30 stress 0.2503474
## Run 31 stress 0.2495342
## Run 32 stress 0.2505651
## Run 33 stress 0.249477
## Run 34 stress 0.2512375
## Run 35 stress 0.2476912
## ... New best solution
## ... Procrustes: rmse 0.0116044 max resid 0.1164874
## Run 36 stress 0.250726
## Run 37 stress 0.2547653
## Run 38 stress 0.2509658
## Run 39 stress 0.2486622
## Run 40 stress 0.2501641
## Run 41 stress 0.2605503
## Run 42 stress 0.2502185
## Run 43 stress 0.2530498
## Run 44 stress 0.2510246
## Run 45 stress 0.2521419
## Run 46 stress 0.2532983
## Run 47 stress 0.2520107
## Run 48 stress 0.248927
## Run 49 stress 0.2496182
## Run 50 stress 0.2544681
## Run 51 stress 0.2556238
## Run 52 stress 0.2506974
## Run 53 stress 0.2499139
## Run 54 stress 0.2516367
## Run 55 stress 0.2478359
## ... Procrustes: rmse 0.01063526 max resid 0.09987672
## Run 56 stress 0.2504118
## Run 57 stress 0.2526479
## Run 58 stress 0.2528266
## Run 59 stress 0.2513185
## Run 60 stress 0.2512818
## Run 61 stress 0.2513904
## Run 62 stress 0.2485547
## Run 63 stress 0.2501745
## Run 64 stress 0.2514013
## Run 65 stress 0.2512837
## Run 66 stress 0.249287
## Run 67 stress 0.2512347
## Run 68 stress 0.2507526
## Run 69 stress 0.2540948
## Run 70 stress 0.2521421
## Run 71 stress 0.2508441
## Run 72 stress 0.2511694
## Run 73 stress 0.25188
## Run 74 stress 0.250495
## Run 75 stress 0.2549259
## Run 76 stress 0.2503426
## Run 77 stress 0.2495167
## Run 78 stress 0.2502347
## Run 79 stress 0.2498117
## Run 80 stress 0.2562333
## Run 81 stress 0.2473761
## ... New best solution
## ... Procrustes: rmse 0.009005609 max resid 0.09090727
## Run 82 stress 0.2510267
## Run 83 stress 0.2499847
## Run 84 stress 0.2498365
## Run 85 stress 0.2513051
## Run 86 stress 0.2518461
## Run 87 stress 0.2498754
## Run 88 stress 0.2510431
## Run 89 stress 0.2518924
## Run 90 stress 0.2497324
## Run 91 stress 0.2528186
## Run 92 stress 0.2516808
## Run 93 stress 0.2505357
## Run 94 stress 0.2499259
## Run 95 stress 0.2523702
## Run 96 stress 0.2487998
## Run 97 stress 0.252898
## Run 98 stress 0.255095
## Run 99 stress 0.2512048
## Run 100 stress 0.2527065
## *** Best solution was not repeated -- monoMDS stopping criteria:
## 11: no. of iterations >= maxit
## 53: stress ratio > sratmax
## 36: scale factor of the gradient < sfgrminnmds$stress # Should be <0.## [1] 0.2473761# Prepare NMDS points + metadata
nmds_points <- as.data.frame(nmds$points)
nmds_points$Plate <- rownames(nmds_points)
plot_data <- metadata %>%
inner_join(nmds_points, by = "Plate")TREATMENT
REGION
SITE
SEASON
Permanova_Abundance
## Permutation test for adonis under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 100
##
## adonis2(formula = community_matrix ~ Treatment * Season * Region, data = metadata, permutations = 100, method = "bray")
## Df SumOfSqs R2 F Pr(>F)
## Treatment 2 7.922 0.03725 16.1875 0.009901 **
## Season 1 7.879 0.03705 32.2001 0.009901 **
## Region 2 35.157 0.16533 71.8377 0.009901 **
## Treatment:Season 2 1.478 0.00695 3.0200 0.009901 **
## Treatment:Region 4 8.165 0.03839 8.3414 0.009901 **
## Season:Region 2 9.063 0.04262 18.5191 0.009901 **
## Treatment:Season:Region 4 2.040 0.00959 2.0841 0.009901 **
## Residual 576 140.947 0.66281
## Total 593 212.651 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
SESSILE INVERTEBRATES COMMUNITY STRUCTURE
Heat-maps for abundance per layer
